How Async in Python differs from Threading or Multiprocessing

🚀 Day 44 of my Python Learning Journey 🚀 Today, I explored problem-solving using recursion. 🔁 Recursion is a powerful technique where a function calls itself to solve smaller instances of a problem until it reaches a base case. It’s especially useful for tasks like factorial calculation, Fibonacci series, tree traversals, and many divide-and-conquer algorithms. Key takeaways from today’s learning: ✅ Breaking complex problems into smaller subproblems ✅ Importance of base conditions to avoid infinite loops ✅ Improved understanding of problem-solving patterns Every day, I’m realizing how Python not only simplifies coding but also sharpens logical thinking. 🐍✨ #Python #100DaysOfCode #LearningJourney #Recursion #ProblemSolving

As someone who sometimes builds with Python, I’m really excited about its new free threaded mode. According to some benchmarks, performance can increase by up to 5 times, which is impressive. In some cases this multi threaded mode even outperforms the traditional multi process approach. Python keeps proving it’s not just relevant, it’s everywhere, powering systems, research, and tools across industries. I’m looking forward to seeing how teams use this new power to drive innovation and efficiency. What’s your favorite new Python feature? #Python #SoftwareEngineering #AI #Automation #DevCommunity #TechInnovation #Programming #Python314 #OpenSource

This week, I've been focused on learning about data processing using python. A tangible win was swapping out a classic for loop for a vectorized numpy.where() operation to create a new conditional column in a large Pandas DataFrame. The performance gain was immediate. How this worked in backend? Vectorization executes operations in optimized, pre-compiled C code, drastically reducing processing time. This means faster feature engineering, quicker model iteration, and more scalable data pipelines. It's a fundamental shift from how to code to how to code efficiently for data-intensive tasks. This small change makes a huge difference in building faster, more efficient ML workflows. #Python #DataScience #MachineLearning #Pandas #NumPy #Developer

I have been asked about moving notebooks to production countless times. This is why scripting is a better place to start for end-to-end development education.

🧠 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗦𝗰𝗼𝗽𝗲 𝘄𝗶𝘁𝗵 𝗟𝗘𝗚𝗕 I spent some time exploring how Python decides which variable to use when the same name appears in different places. This is all about the 𝗟𝗘𝗚𝗕 𝗿𝘂𝗹𝗲 — Local → Enclosing → Global → Built-in. Through my practice, I explored variables at different levels of scope and learned how Python searches from the innermost scope outward. Using techniques like globals()['x'] helped me clearly see how global variables can be accessed even inside nested functions. Small exercises like this sharpen my understanding of Python’s core behavior and make me more confident in writing clean, predictable code. #Python #coding #CodeLearning #DeveloperJourney #AIEngineer #CreativeAlchemist

👨💻 Day 31 of my Python learning journey Today, I revisited Polymorphism - one of the most flexible and powerful principles in Object-Oriented Programming. 🔍 What I learned: ✅ Polymorphism means “many forms” - the same function or method can behave differently depending on the object it is acting upon. ✅ It allows one interface to be used for different data types or classes. ✅ In Python, it makes code more dynamic, scalable, and easy to maintain. 💡 Real-World Analogy: Think of the word “run”: A person can run 🏃♂️ A car engine can run 🚗 A program can run 💻 The same action, but different meanings - that’s Polymorphism! ⚙️ Key Takeaways: Promotes flexibility and reusability. Makes systems adaptable to future changes. Reduces code duplication and increases maintainability. 🚀 Learning Insight: Polymorphism isn’t just about shared methods - it’s about designing code that can adapt without breaking. #Python #Day31 #OOP #Polymorphism #LearningPython #AI #ML #FresherInTech #CodingJourney #LinkedInLearning #TechWithSuhit #100DaysOfCode

🚀 Set in Python - A Set in Python is a collection data type that is unordered, unindexed, and contains unique elements. It is mainly used when you want to store non-duplicate items and perform mathematical set operations like union, intersection, and difference. 🧩 Key Features: ▪️ Unordered: Elements have no defined order. ▪️ Mutable: You can add or remove items after creation. ▪️ No duplicates: Automatically removes repeated elements. ▪️ Supports set operations like union(), intersection(), difference(), etc. 💡 When to Use: 🔸 You need unique values. 🔸 You want to perform fast membership testing. 🔸 You need set-based operations (like finding common elements). #Python #PythonLearning #PythonBasics #DataStructures #Coding #LearnPython #SetInPython

Did you know? In 2025, Python remains the #1 programming language for data science, with over 90% of data scientists preferring it for tasks like data cleaning, analysis, and machine learning. 🐍📊 It's not just a trend; it's a revolution! 🚀 #Python #DataScience #MachineLearning #TrendingNow #TechTrivia #DataDriven